Bibliographic record
Abstract
Few leaders excel at both the unit and enterprise levels. More than ever, though, corporations need people capable of running business units, functions, or regions and focusing on broader company goals. It's up to organizations to develop leaders who can manage the inherent tensions between unit and enterprise priorities. Take the example of RBC Financial Group, one of the largest, most profitable companies in Canada. In the mid-1990's, RBC revamped its competitive strategy in a couple of ways. After the government announced that the Big Six banks in Canada could neither merge with nor acquire one another, RBC decided to grow through cross-border acquisitions. Additionally, because customers were starting to seek bundled products and services, RBC reached across its traditional stand-alone businesses to offer integrated solutions. These changes in strategy didn't elicit immediate companywide support. Instinctively, employees reacted against what would amount to a delicate balancing act: They would have to lift their focus out of their silos while continuing to meet unit goals. However, by communicating extensively with staff members, cross-fertilizing talent across unit boundaries, and targeting rewards to shape performance, RBC was able to cultivate rising leaders with the unit expertise and the enterprise vision to help the company fulfill its new aims. Growing such well-rounded leaders takes sustained effort because unit-enterprise tensions are quite real. Three common conditions reinforce these tensions. First, most organizational structures foster silo thinking and unimaginative career paths. Second, most companies lack venues for airing and resolving conflicts that arise when there are competing priorities. Third, many have misguided reward systems that pit unit performance against enterprise considerations. Such long-established patterns of organizational behavior are tough to break. Fortunately, as RBC discovered, people can be trained to think and work differently.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.055 | 0.066 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".